3 papers
cs.CL2024
Towards Massive Multilingual Holistic Bias
Xiaoqing Ellen Tan, Prangthip Hansanti, Carleigh Wood +3
In the current landscape of automatic language generation, there is a need to understand, evaluate, and mitigate demographic biases as existing models are becoming increasingly mul…
cs.CL2024
Efficient Tool Use with Chain-of-Abstraction Reasoning
Silin Gao, Jane Dwivedi-Yu, Ping Yu +7
To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and ph…
cs.CL2023
ROBBIE: Robust Bias Evaluation of Large Generative Language Models
David Esiobu, Xiaoqing Tan, Saghar Hosseini +7
As generative large language models (LLMs) grow more performant and prevalent, we must develop comprehensive enough tools to measure and improve their fairness. Different prompt-ba…